Diseases outbreak dataset exploration

by Ahmed Hanafi

Preliminary Wrangling

• the data is about zoonotic notifiable diseases outbreak that is recorded by FAO and could be found here
• the data was downloaded in separate files that were apended to one file and columns were renamed

# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
import pandas_bokeh
import plotly.express as px
pandas_bokeh.output_notebook()
%matplotlib inline

Loading BokehJS ...

df = pd.read_csv('outbreak_all_data_new.csv')
df['reporting_date']=pd.to_datetime(df.reporting_date)
df['month_year'] = df['reporting_date'].dt.to_period('M').astype('string')
df.fillna(0,inplace=True)
#make data_frame of aggregates for each disease
diseases_sum = df.groupby('disease').sum().reset_index()
#calculate case fatality for human and animals
diseases_sum['human_case_fatality']= diseases_sum.humans_deaths/diseases_sum.humans_affected
diseases_sum['animal_case_fatality']= diseases_sum.sum_deaths/diseases_sum.sum_cases
#make data_frame of aggregates for each country
country_sum = df.groupby('country').sum().reset_index()
country_sum['human_case_fatality']= country_sum.humans_deaths/country_sum.humans_affected
country_sum['animal_case_fatality']= country_sum.sum_deaths/country_sum.sum_cases

What is the structure of your dataset?

print ('this data set has '+ str(df.shape[1]) +' columns and ' + str(df.shape[0]) + ' rows')

this data set has 25 columns and 90000 rows

What is/are the main feature(s) of interest in your dataset?

the dataset has diseases incedence and there human and animal infections or deathes

What features in the dataset do you think will help support your investigation into your feature(s) of interest?

the data include location and and could help mab out feature

Univariate Exploration

humans_affected distribution with log scale

df.humans_affected.plot_bokeh(kind='hist',bins = range(0,501,10),vertical_xlabel = True,figsize=(1000,500),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '1002', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='1013', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='1016', ...), Grid(id='1020', ...), Legend(id='1049', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='1017', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='1039', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1003', ...),

title_location = 'above',

toolbar = Toolbar(id='1028', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='1005', ...),

x_scale = LinearScale(id='1009', ...),

y_range = Range1d(id='1007', ...),

y_scale = LogScale(id='1011', ...))

let's dig deeper

df.humans_affected.plot_bokeh(kind='hist',vertical_xlabel = True,xlim = (0,20),bins = range(0,21),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '1101', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='1112', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='1115', ...), Grid(id='1119', ...), Legend(id='1148', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='1116', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 400,

plot_width = 600,

renderers = [GlyphRenderer(id='1138', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1102', ...),

title_location = 'above',

toolbar = Toolbar(id='1127', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = Range1d(id='1104', ...),

x_scale = LinearScale(id='1108', ...),

y_range = Range1d(id='1106', ...),

y_scale = LogScale(id='1110', ...))

print('the mode of humans_affected is '+ str(int(df.humans_affected.mode()))+ '\nmax humans_affected is '+str(int(df.humans_affected.max()))+'\nhumans_affected column has : '+str(df.humans_affected.isnull().sum())+' nulls')

the mode of humans_affected is 0
max humans_affected is 495
humans_affected column has : 0 nulls

most of the records of human affected if existed is 1 which is a good thing but most of this column is null and I think null mean Zero so I filled it with Zero there is also outliers where we have around 500 humans affected which raise a concern we need to see which diseases cause that

human_age distribution with log scale

df.humans_age.plot_bokeh(kind='hist',bins=range(0,100),figsize=(1000,500),vertical_xlabel = True,logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '1207', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='1218', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='1221', ...), Grid(id='1225', ...), Legend(id='1254', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='1222', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='1244', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1208', ...),

title_location = 'above',

toolbar = Toolbar(id='1233', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='1210', ...),

x_scale = LinearScale(id='1214', ...),

y_range = Range1d(id='1212', ...),

y_scale = LogScale(id='1216', ...))

print('the mode of humans_age is '+ str(int(df.humans_age.mode()))+ '\nmax humans_age is '+str(int(df.humans_age.max()))+'\nhumans_age column has : '+str(df.humans_age.isnull().sum())+' nulls')

the mode of humans_age is 0
max humans_age is 96
humans_age column has : 0 nulls

most of human's cases are less than 1 year age or they are Zero because most of them was null

human_deaths distribution with log scale

df.humans_deaths.plot_bokeh(kind='hist',figsize=(1000,500),vertical_xlabel = True,bins =range(0,137,2),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '1320', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='1331', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='1334', ...), Grid(id='1338', ...), Legend(id='1367', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='1335', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='1357', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1321', ...),

title_location = 'above',

toolbar = Toolbar(id='1346', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='1323', ...),

x_scale = LinearScale(id='1327', ...),

y_range = Range1d(id='1325', ...),

y_scale = LogScale(id='1329', ...))

print('the mode of humans_deaths is '+ str(int(df.humans_deaths.mode()))+ '\nmax humans_deaths is '+str(int(df.humans_deaths.max()))+'\nhumans_deaths column has : '+str(df.humans_deaths.isnull().sum())+' nulls')

the mode of humans_deaths is 0
max humans_deaths is 134
humans_deaths column has : 0 nulls

the mode of humans_deaths if existed is 1, some times there is high number of deaths which raise concern we need to see which diseases cause that and most of the column is null which I think mean Zero so I filled it with Zero

sum_cases of animals distribution with log scale

df.sum_cases.plot_bokeh(kind='hist',vertical_xlabel = True,bins=range(0,900001,10000),figsize=(1000,500),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '1440', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='1451', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='1454', ...), Grid(id='1458', ...), Legend(id='1487', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='1455', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='1477', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1441', ...),

title_location = 'above',

toolbar = Toolbar(id='1466', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='1443', ...),

x_scale = LinearScale(id='1447', ...),

y_range = Range1d(id='1445', ...),

y_scale = LogScale(id='1449', ...))

let's dig deeper

df.sum_cases.plot_bokeh(kind='hist',vertical_xlabel = True,xlim=(0,1000),bins=range(0,1000,10),figsize=(1000,500),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '1567', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='1578', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='1581', ...), Grid(id='1585', ...), Legend(id='1614', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='1582', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='1604', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1568', ...),

title_location = 'above',

toolbar = Toolbar(id='1593', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = Range1d(id='1570', ...),

x_scale = LinearScale(id='1574', ...),

y_range = Range1d(id='1572', ...),

y_scale = LogScale(id='1576', ...))

let's dig deeper

df.sum_cases.plot_bokeh(kind='hist',vertical_xlabel = True,xlim=(0,100),bins=range(0,101),figsize=(1000,500),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '1701', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='1712', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='1715', ...), Grid(id='1719', ...), Legend(id='1748', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='1716', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='1738', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1702', ...),

title_location = 'above',

toolbar = Toolbar(id='1727', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = Range1d(id='1704', ...),

x_scale = LinearScale(id='1708', ...),

y_range = Range1d(id='1706', ...),

y_scale = LogScale(id='1710', ...))

print('the mode of sum_cases of animals is '+ str(int(df.sum_cases.mode()))+ '\nmax sum_cases of animals is '+str(int(df.sum_cases.max()))+'\nsum_cases column has : '+str(df.sum_cases.isnull().sum())+' nulls')

the mode of sum_cases of animals is 1
max sum_cases of animals is 800000
sum_cases column has : 0 nulls

most of sum_cases of animal is less than 10 and the mode is 1 which is a good thing but there is cases where it is too large at 800K and I think those cases are in poultry, we need to see which diseases cause that

sum_destroyed animals distribution with log scale

df.sum_destroyed.plot_bokeh(kind='hist',vertical_xlabel = True,figsize=(1000,500),bins=range(0,3750000,50000),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '1842', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='1853', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='1856', ...), Grid(id='1860', ...), Legend(id='1889', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='1857', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='1879', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1843', ...),

title_location = 'above',

toolbar = Toolbar(id='1868', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='1845', ...),

x_scale = LinearScale(id='1849', ...),

y_range = Range1d(id='1847', ...),

y_scale = LogScale(id='1851', ...))

let's dig deeper

df.sum_destroyed.plot_bokeh(kind='hist',vertical_xlabel = True,figsize=(1000,500),bins=range(0,50000,500),xlim=(0,50000),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '1990', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='2001', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='2004', ...), Grid(id='2008', ...), Legend(id='2037', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='2005', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='2027', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='1991', ...),

title_location = 'above',

toolbar = Toolbar(id='2016', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = Range1d(id='1993', ...),

x_scale = LinearScale(id='1997', ...),

y_range = Range1d(id='1995', ...),

y_scale = LogScale(id='1999', ...))

let's dig deeper

df.sum_destroyed.plot_bokeh(kind='hist',vertical_xlabel = True,figsize=(1000,500),bins=range(0,500,5),xlim=(0,500),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '2145', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='2156', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='2159', ...), Grid(id='2163', ...), Legend(id='2192', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='2160', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='2182', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='2146', ...),

title_location = 'above',

toolbar = Toolbar(id='2171', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = Range1d(id='2148', ...),

x_scale = LinearScale(id='2152', ...),

y_range = Range1d(id='2150', ...),

y_scale = LogScale(id='2154', ...))

print('the mode of sum_destroyed of animals is '+ str(int(df.sum_destroyed.mode()))+ '\nmax sum_destroyed of animals is '+str(int(df.sum_destroyed.max()))+'\nsum_destroyed column has : '+str(df.sum_destroyed.isnull().sum())+' nulls')

the mode of sum_destroyed of animals is 0
max sum_destroyed of animals is 3660000
sum_destroyed column has : 0 nulls

sum_destroyed sometimes reach realy high numbers as 3.66 million which raise concerne and I think those cases are in poultry, we need to see which diseases cause that, and I think those nulls mean Zero so I filled them with Zero

sum_slaughtered animals distribution with log scale

df.sum_slaughtered.plot_bokeh(kind='hist',vertical_xlabel = True,bins=range(0,900000,10000),figsize=(1000,500),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '2307', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='2318', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='2321', ...), Grid(id='2325', ...), Legend(id='2354', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='2322', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='2344', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='2308', ...),

title_location = 'above',

toolbar = Toolbar(id='2333', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='2310', ...),

x_scale = LinearScale(id='2314', ...),

y_range = Range1d(id='2312', ...),

y_scale = LogScale(id='2316', ...))

let's dig deeper

df.sum_slaughtered.plot_bokeh(kind='hist',vertical_xlabel = True,bins=range(0,10000,100),figsize=(1000,500),xlim=(0,10000),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '2476', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='2487', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='2490', ...), Grid(id='2494', ...), Legend(id='2523', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='2491', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='2513', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='2477', ...),

title_location = 'above',

toolbar = Toolbar(id='2502', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = Range1d(id='2479', ...),

x_scale = LinearScale(id='2483', ...),

y_range = Range1d(id='2481', ...),

y_scale = LogScale(id='2485', ...))

let's dig deeper

df.sum_slaughtered.plot_bokeh(kind='hist',vertical_xlabel = True,bins=range(0,100,1),figsize=(1000,500),xlim=(0,100),logy=True,ylim=(0.1,df.shape[0]),zooming =False)

Figure(

id = '2652', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='2663', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='2666', ...), Grid(id='2670', ...), Legend(id='2699', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LogAxis(id='2667', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 1000,

renderers = [GlyphRenderer(id='2689', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='2653', ...),

title_location = 'above',

toolbar = Toolbar(id='2678', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = Range1d(id='2655', ...),

x_scale = LinearScale(id='2659', ...),

y_range = Range1d(id='2657', ...),

y_scale = LogScale(id='2661', ...))

print('the mode of sum_slaughtered of animals is '+ str(int(df.sum_slaughtered.mode()))+ '\nmax sum_slaughtered of animals is '+str(int(df.sum_slaughtered.max()))+'\nsum_slaughtered column has : '+str(df.sum_slaughtered.isnull().sum())+' nulls')

the mode of sum_slaughtered of animals is 0
max sum_slaughtered of animals is 850816
sum_slaughtered column has : 0 nulls

most of the column is null and I think it mean Zero as the mode but there is high outliers reaching near a million which need more investigation

diseases distribution

df.disease.value_counts().sort_values().plot_bokeh(kind='barh',figsize=(950,500),legend ='bottom_right')

Figure(

id = '2871', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='2882', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='2885', ...), Grid(id='2889', ...), Legend(id='2919', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='2886', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 950,

renderers = [GlyphRenderer(id='2910', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='2872', ...),

title_location = 'above',

toolbar = Toolbar(id='2897', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='2874', ...),

x_scale = LinearScale(id='2878', ...),

y_range = DataRange1d(id='2876', ...),

y_scale = LinearScale(id='2880', ...))

the highest count is for Avian_influenza flowed by African swine fever and viral diseases are the highest incidence

country distribution

df.country.value_counts().sort_values().plot_bokeh(kind='barh',figsize=(950,1750),legend ='bottom_right',zooming=False)

Figure(

id = '3091', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='3102', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='3105', ...), Grid(id='3109', ...), Legend(id='3139', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='3106', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 1750,

plot_width = 950,

renderers = [GlyphRenderer(id='3130', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='3092', ...),

title_location = 'above',

toolbar = Toolbar(id='3117', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='3094', ...),

x_scale = LinearScale(id='3098', ...),

y_range = DataRange1d(id='3096', ...),

y_scale = LinearScale(id='3100', ...))

the highest countries by order are Poland, China and that is expected, Romania, France, Indonesia, Greece, and Egypt

region distribution

df.region.value_counts().sort_values().plot_bokeh(kind='barh',legend ='bottom_right',zooming=False)

Figure(

id = '3318', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='3329', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='3332', ...), Grid(id='3336', ...), Legend(id='3366', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='3333', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 400,

plot_width = 600,

renderers = [GlyphRenderer(id='3357', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='3319', ...),

title_location = 'above',

toolbar = Toolbar(id='3344', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='3321', ...),

x_scale = LinearScale(id='3325', ...),

y_range = DataRange1d(id='3323', ...),

y_scale = LinearScale(id='3327', ...))

Europe has the most records flowed by Asia

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?

• most numerical are extremely right skewed.
• there were extremely high deaths and cases as outliers which need more investigation.
• I preferred to use log scale so that all values appear

Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

there were high outliers which need more investigation and I did not perform any changes but the columns' names

Bivariate Exploration

disease vs sum humans_affected

df.groupby('disease').humans_affected.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500))

Figure(

id = '3551', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='3562', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='3565', ...), Grid(id='3569', ...), Legend(id='3599', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='3566', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 950,

renderers = [GlyphRenderer(id='3590', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='3552', ...),

title_location = 'above',

toolbar = Toolbar(id='3577', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='3554', ...),

x_scale = LinearScale(id='3558', ...),

y_range = DataRange1d(id='3556', ...),

y_scale = LinearScale(id='3560', ...))

disease vs sum humans_deaths

df.groupby('disease').humans_deaths.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500))

Figure(

id = '3790', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='3801', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='3804', ...), Grid(id='3808', ...), Legend(id='3838', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='3805', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 950,

renderers = [GlyphRenderer(id='3829', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='3791', ...),

title_location = 'above',

toolbar = Toolbar(id='3816', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='3793', ...),

x_scale = LinearScale(id='3797', ...),

y_range = DataRange1d(id='3795', ...),

y_scale = LinearScale(id='3799', ...))

sum humans_affected through months

df.groupby('month_year').humans_affected.sum().plot_bokeh(kind='line',figsize=(2000,500),vertical_xlabel = True,zooming=False)

Figure(

id = '3999', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='4010', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='4013', ...), Grid(id='4017', ...), Legend(id='4047', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='4014', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 2000,

renderers = [GlyphRenderer(id='4038', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='4000', ...),

title_location = 'above',

toolbar = Toolbar(id='4025', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='4002', ...),

x_scale = LinearScale(id='4006', ...),

y_range = DataRange1d(id='4004', ...),

y_scale = LinearScale(id='4008', ...))

case fatality per disease for human

px.bar(diseases_sum.fillna(0).query('human_case_fatality != 0').sort_values(['human_case_fatality'],ascending=False),x='disease',y='human_case_fatality')

• leptospirosis has the highest number of human cases followed by the same four viral diseases which have the highest number deaths which are japanese encephalitis, MERS-CoV, Avian-Influenza, and Rift Valley Fever
• most diseases have Zero human cases and deaths
• Brucellosis has one case in human which is so weird as I am sure that the number is higher than that by a lot
• there were two high spikes in 2013
• Rabies has the highest case fatality for human followed by MERS-CoV

diseases vs sum of animal cases

df.groupby('disease').sum_cases.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500),disable_scientific_axes='x')

Figure(

id = '4250', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='4261', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='4264', ...), Grid(id='4268', ...), Legend(id='4298', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='4265', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 950,

renderers = [GlyphRenderer(id='4289', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='4251', ...),

title_location = 'above',

toolbar = Toolbar(id='4276', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='4253', ...),

x_scale = LinearScale(id='4257', ...),

y_range = DataRange1d(id='4255', ...),

y_scale = LinearScale(id='4259', ...))

let's dig without poultry diseases and African swine fever

df.query('disease !="Influenza - Avian" and disease !="Newcastle disease" and disease !="African swine fever"')\
.groupby('disease').sum_cases.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500),disable_scientific_axes='x')

Figure(

id = '4508', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='4519', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='4522', ...), Grid(id='4526', ...), Legend(id='4556', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='4523', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 950,

renderers = [GlyphRenderer(id='4547', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='4509', ...),

title_location = 'above',

toolbar = Toolbar(id='4534', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='4511', ...),

x_scale = LinearScale(id='4515', ...),

y_range = DataRange1d(id='4513', ...),

y_scale = LinearScale(id='4517', ...))

diseases vs sum of animal deaths

df.groupby('disease').sum_deaths.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500),disable_scientific_axes='x')

Figure(

id = '4772', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='4783', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='4786', ...), Grid(id='4790', ...), Legend(id='4820', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='4787', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 950,

renderers = [GlyphRenderer(id='4811', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='4773', ...),

title_location = 'above',

toolbar = Toolbar(id='4798', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='4775', ...),

x_scale = LinearScale(id='4779', ...),

y_range = DataRange1d(id='4777', ...),

y_scale = LinearScale(id='4781', ...))

let's dig without poultry diseases and African swine fever

df.query('disease !="Influenza - Avian" and disease !="Newcastle disease" and disease !="African swine fever"')\
.groupby('disease').sum_deaths.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500),disable_scientific_axes='x')

Figure(

id = '5042', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='5053', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='5056', ...), Grid(id='5060', ...), Legend(id='5090', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='5057', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 950,

renderers = [GlyphRenderer(id='5081', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='5043', ...),

title_location = 'above',

toolbar = Toolbar(id='5068', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='5045', ...),

x_scale = LinearScale(id='5049', ...),

y_range = DataRange1d(id='5047', ...),

y_scale = LinearScale(id='5051', ...))

diseases vs sum of animal slaughtered

df.groupby('disease').sum_slaughtered.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500),disable_scientific_axes='x')

Figure(

id = '5318', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='5329', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='5332', ...), Grid(id='5336', ...), Legend(id='5366', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='5333', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

match_aspect = False,

max_height = None,

max_width = None,

min_border = 5,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 950,

renderers = [GlyphRenderer(id='5357', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='5319', ...),

title_location = 'above',

toolbar = Toolbar(id='5344', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='5321', ...),

x_scale = LinearScale(id='5325', ...),

y_range = DataRange1d(id='5323', ...),

y_scale = LinearScale(id='5327', ...))

let's dig without poultry diseases and African swine fever

df.query('disease !="Influenza - Avian" and disease !="Newcastle disease" and disease !="African swine fever"')\
.groupby('disease').sum_slaughtered.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500),disable_scientific_axes='x')

Figure(

id = '5600', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='5611', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='5614', ...), Grid(id='5618', ...), Legend(id='5648', ...)],

css_classes = [],

disabled = False,

extra_x_ranges = {},

extra_y_ranges = {},

frame_height = None,

frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='5615', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = (0, 0, 0, 0),

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max_height = None,

max_width = None,

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min_border_right = None,

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outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

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outline_line_join = 'bevel',

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plot_height = 500,

plot_width = 950,

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sizing_mode = 'fixed',

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toolbar_location = 'right',

toolbar_sticky = True,

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width_policy = 'auto',

x_range = DataRange1d(id='5603', ...),

x_scale = LinearScale(id='5607', ...),

y_range = DataRange1d(id='5605', ...),

y_scale = LinearScale(id='5609', ...))

diseases vs sum of animal destroyed

df.groupby('disease').sum_destroyed.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500),disable_scientific_axes='x')

Figure(

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outline_line_join = 'bevel',

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width_policy = 'auto',

x_range = DataRange1d(id='5891', ...),

x_scale = LinearScale(id='5895', ...),

y_range = DataRange1d(id='5893', ...),

y_scale = LinearScale(id='5897', ...))

let's dig without poultry diseases and African swine fever

df.query('disease !="Influenza - Avian" and disease !="Newcastle disease" and disease !="African swine fever"')\
.groupby('disease').sum_destroyed.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,500),disable_scientific_axes='x')

Figure(

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align = 'start',

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min_width = None,

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outline_line_alpha = 1.0,

outline_line_cap = 'butt',

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outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 950,

renderers = [GlyphRenderer(id='6221', ...)],

reset_policy = 'standard',

right = [],

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subscribed_events = [],

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title_location = 'above',

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toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='6185', ...),

x_scale = LinearScale(id='6189', ...),

y_range = DataRange1d(id='6187', ...),

y_scale = LinearScale(id='6191', ...))

poultry diseases dominate the numbers followed by African swine fever and other diseases of pig and ruminant

sum_cases per month

df.groupby('month_year').sum_cases.sum().plot_bokeh(kind='line',figsize=(2000,500),vertical_xlabel = True,zooming=False)

Figure(

id = '6446', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

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border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='6460', ...), Grid(id='6464', ...), Legend(id='6494', ...)],

css_classes = [],

disabled = False,

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frame_width = None,

height = None,

height_policy = 'auto',

hidpi = True,

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left = [LinearAxis(id='6461', ...)],

lod_factor = 10,

lod_interval = 300,

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lod_timeout = 500,

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max_height = None,

max_width = None,

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min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 500,

plot_width = 2000,

renderers = [GlyphRenderer(id='6485', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

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title = Title(id='6447', ...),

title_location = 'above',

toolbar = Toolbar(id='6472', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='6449', ...),

x_scale = LinearScale(id='6453', ...),

y_range = DataRange1d(id='6451', ...),

y_scale = LinearScale(id='6455', ...))

there are two spikes in the year 2006 and 2012

case fatality per disease for animals

px.bar(diseases_sum.fillna(0).query('animal_case_fatality != 0').sort_values(['animal_case_fatality']),x='animal_case_fatality',y='disease',width=950,height=650)

the highest case fatality is for Schmallenberg followed by Anthrax

country vs humans_affected sum

df.groupby('country').humans_affected.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,1750))

Figure(

id = '6751', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='6762', ...)],

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border_fill_color = '#ffffff',

center = [Grid(id='6765', ...), Grid(id='6769', ...), Legend(id='6799', ...)],

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lod_timeout = 500,

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max_height = None,

max_width = None,

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min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

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outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'webgl',

plot_height = 1750,

plot_width = 950,

renderers = [GlyphRenderer(id='6790', ...)],

reset_policy = 'standard',

right = [],

sizing_mode = 'fixed',

subscribed_events = [],

tags = [],

title = Title(id='6752', ...),

title_location = 'above',

toolbar = Toolbar(id='6777', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='6754', ...),

x_scale = LinearScale(id='6758', ...),

y_range = DataRange1d(id='6756', ...),

y_scale = LinearScale(id='6760', ...))

country vs human_deaths sum

df.groupby('country').humans_deaths.sum().sort_values().plot_bokeh(kind='barh',zooming =False,legend ='bottom_right',figsize=(950,1750))

Figure(

id = '7062', …)

above = [],

align = 'start',

aspect_ratio = None,

aspect_scale = 1,

background = None,

background_fill_alpha = 1.0,

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below = [LinearAxis(id='7073', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='7076', ...), Grid(id='7080', ...), Legend(id='7110', ...)],

css_classes = [],

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outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

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outline_line_width = 1,

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plot_height = 1750,

plot_width = 950,

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title_location = 'above',

toolbar = Toolbar(id='7088', ...),

toolbar_location = 'right',

toolbar_sticky = True,

visible = True,

width = None,

width_policy = 'auto',

x_range = DataRange1d(id='7065', ...),

x_scale = LinearScale(id='7069', ...),

y_range = DataRange1d(id='7067', ...),

y_scale = LinearScale(id='7071', ...))

human_case_fatality per country

px.bar(country_sum.fillna(0).query('human_case_fatality !=0').sort_values(['human_case_fatality']),x='human_case_fatality',y='country',height=1000)

• most of the countries have very litle cases and deaths, the highest in cases is Sri Lanka followed by India then Saudi Arabia, the last two metioned are the highest in human deaths with the same order, and I think MERS-CoV is to blame on the high cases and deaths in Saudi Arabia
• some countries have a case fatality of one which is shocking may be there data is not complete

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?

• poultry diseases have the highest number animal of cases and deaths followed by pig and then ruminants
• most diseases have Zero human deaths while the highest number of human deathes is caused by viral diseases like japanese encephalitis, MERS-CoV, Avian-Influenza, and Rift Valley Fever
• there are two spikes in the year 2006 and 2012 in sum_cases

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?

No

Multivariate Exploration

sum_cases through time for different diseases

px.line(df.groupby(['month_year','disease']).sum().reset_index(),x='month_year',y='sum_cases',color='disease')

as we can see the spikes are caused by poultry diseases mostly

animal cases and deaths per disease

px.bar(diseases_sum,x='disease',y=[diseases_sum.sum_cases,diseases_sum.sum_deaths],barmode='group',log_y=True)

some diseases have vey high cases and deaths as Avian Influenza and Newcastle disease while most have both of them low but MERS-CoV have has zero deaths for animal desbite it's high fatality in humans

human cases and deaths per disease

#select records that are not zero to represent
dise_sum_human = diseases_sum.query('humans_affected != 0')
px.bar(dise_sum_human,x='disease',y=[dise_sum_human.humans_affected,dise_sum_human.humans_deaths],barmode='group',log_y=True)

sum humans_affected through time for different diseases

px.line(df.groupby(['month_year','disease']).sum().query('humans_affected != 0').reset_index(),x='month_year',y='humans_affected',color='disease')

• leptospirosis has high humans' incedince and zero deathes desbite being a dangerous disease while japanese encephalitis, MERS-CoV, Avian-Influenza, and Rift Valley Fever have less incedince than it but higher mortality
• both spikes breviously opserved in 2013 are because of leptospirosis mostly

human cases and deaths per country

#select records that are not zero to represent
country_sum_human = country_sum.query('humans_affected != 0')
px.bar(country_sum_human,y='country',x=[country_sum_human.humans_affected,country_sum_human.humans_deaths],barmode='group',height=1750,log_x=True)

• desbite Sri Lanka having the heighest number of cases it has zero fatality
• as we can see those country with high case_fatality have 1 up to 4 cases each which confirm incomplete data even more

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?

• looking at cases beside deaths added another prespective comparing diseases
• ploting diseases across time show which disease cause spikes in incedince over time

Were there any interesting or surprising interactions between features?

• desbite Sri Lanka having the heighest number of cases it has zero fatality
• leptospirosis has high humans' incedince and zero deathes desbite being a dangerous disease while japanese encephalitis, MERS-CoV, Avian-Influenza, and Rift Valley Fever have less incedince than it but higher mortality